ISSN 1004-4140
CN 11-3017/P

基于子空间投影和边缘增强的低剂量CT去噪

魏屹立, 杨子元, 夏文军, 汪涛, 张意

魏屹立, 杨子元, 夏文军, 等. 基于子空间投影和边缘增强的低剂量CT去噪[J]. CT理论与应用研究, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108.
引用本文: 魏屹立, 杨子元, 夏文军, 等. 基于子空间投影和边缘增强的低剂量CT去噪[J]. CT理论与应用研究, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108.
WEI Y L, YANG Z Y, XIA W J, et al. Low-dose CT denoising based on subspace projection and edge enhancement[J]. CT Theory and Applications, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108. (in Chinese).
Citation: WEI Y L, YANG Z Y, XIA W J, et al. Low-dose CT denoising based on subspace projection and edge enhancement[J]. CT Theory and Applications, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108. (in Chinese).

基于子空间投影和边缘增强的低剂量CT去噪

基金项目: 四川大学“从0到1”创新研究项目(终端诊疗驱动的低剂量CT重建理论研究(2022SCUH0016));四川省杰出青年科技人才项目(基于弱监督学习的医学成像方法研究(2021JDJQ0024))
详细信息
    作者简介:

    魏屹立: 男,四川大学计算机科学与技术硕士研究生,主要从事深度学习和医学图像重建的研究,E-mail:umbrellalalalala@qq.com

    张意: 男,四川大学网络空间安全学院教授、博士生导师,主要研究方向为基于机器学习和表示学习的医学成像及计算机视觉,E-mail:yzhang@scu.edu.cn

    通讯作者:

    张意

  • 中图分类号: O  242;TP  391.41

Low-dose CT Denoising Based on Subspace Projection and Edge Enhancement

  • 摘要: 低剂量计算机断层扫描(CT)是一种相对安全的疾病筛查手段,但低剂量CT图像往往包含较多噪声和伪影,严重影响医生的诊断。针对该问题,本文提出一种基于子空间投影和边缘增强网络(SPEENet)。SPEENet为自编码器结构,包含双流编码器和解码器两个主要模块。双流编码器可以被分为噪声图像编码流及边缘信息编码流两部分,噪声图像编码流对低剂量CT图像进行特征提取,利用图像特征去除低剂量CT中的噪声和伪影;边缘信息编码流部分主要关注低剂量CT图像的边缘信息,利用边缘信息保护图像结构。为充分利用编码器特征,本文引入噪声基投影模块,构建基于编码器和解码器特征的基,并利用该基将编码器提取的特征投影到对应的子空间,获取更好的特征表示。本文在公开数据集上进行实验以验证提出网络的有效性,实验结果表明,相较于其他低剂量CT去噪网络,SPEENet可以取得更好的去噪效果。
    Abstract: Low-dose computed tomography (CT) is a relatively safe method for disease screening. But low-dose CT images often contain severe noise and artifacts, which seriously affect the subsequent diagnosis. To solve this problem, this paper proposes a subspace projection and edge enhancement network (SPEENet). SPEENet hold an architecture of autoencoder, including two main modules: dual stream encoder and decoder. The dual stream encoder can be divided into two parts: noise image coding stream and edge information coding stream. The noise image coding stream removes the noise and artifacts in low-dose CT images by using the image features extracted from the low-dose CT images. The edge information coding stream mainly focuses on the edge information of low-dose CT images and fully utilize the edge information to preserve the structures. In order to make full use of the encoder features, this paper introduces the noise basis projection module to establish a basis based on the features of encoder and decoder, and uses this basis to project the features extracted by the encoder into the corresponding subspace to obtain better feature representation. In this paper, experiments are conducted on the public database to verify the effectiveness. The experimental results show that SPEENet can achieve better denoising performance than other low-dose CT denoising networks.
  • 随着年龄的增长,腰椎退行性变及椎间盘病变日趋增多,CT检查能及时发现诊断腰椎病变并能随访治疗效果,但CT检查辐射问题一直为人们所关注,随着患者受辐射剂量的增加,癌症的发生概率会增大,腰椎CT扫描范围包括性腺,而人体性腺对辐射最敏感,所以开展低剂量腰椎CT检查非常必要。

    以往研究均是通过降低管电压或者降低管电流来降低辐射剂量,因腰椎体层较厚,降低管电压或管电流会导致图像噪声增加。本文为解决腰椎CT高辐射剂量及图像噪声偏高的问题,采用最新的能谱纯化技术结合高级模拟迭代重建(ADMIRE)技术,探讨如何更好的优化腰椎CT检查的图像质量和降低辐射剂量。

    选取2021年8月至2022年5月因腰痛来我院行腰椎CT检查的患者,在检查前计算患者的体质量指数(bodymassindex,BMI),BMI=体重(kg)/身高(m)2。纳入年龄在25~65岁,BMI在18.5~25 kg/m2的患者,排除有腰椎手术史和腰椎畸形及有椎体金属植入物的患者,共收集88例。对照组(A组)、试验组(B组)每组44例。

    A组与B组平均年龄分别为(45.9±12.1)岁和(47.2±13.8)岁。两组间年龄差异无统计学意义,A组与B组平均BMI分别为(20.1±2.89)kg/m和(21.40±3.50)kg/m

    采用德国SOMATOM Force第3代双源CT,扫描范围从胸12椎体至骶1椎体。扫描参数:对照组(A组)管电压120 kV,参考管电流350 mAs;试验组(B组)管电压Sn 150 kV,参考管电流350 mAs,其他扫描参数均一致。

    重建采用高级模拟迭代重建算法(ADMIRE),重建等级3级,重建薄层图像,层厚1 mm,层间距0.60 mm,软组织窗采用软组织算法,卷积核Br40,骨窗采用骨算法,卷积核Br64,重建图像窗宽,窗位分别为350 HU和50 HU(软组织窗)、2500 HU和800 HU(骨窗)。所有图像重建完成后自动发至西门子Syngovia VB20A后处理工作站。

    由1名主管技师从工作站中取L3椎体正中层面,在软组织窗上测量腰大肌与竖脊肌的CT值和噪声,腰大肌的噪声为SD1,竖脊肌的噪声为SD2,噪声值用对应所测的标准差表示,并计算信噪比(SNR):

    $$ {\rm{SNR}}=腰大肌\;{\rm{CT}}\;值/{\rm{SD}}1。$$ (1)

    由3名副主任及以上诊断医师双盲法进行评分。评价L3/4层面椎间盘、椎间孔、黄韧带、硬膜囊及小关节图像质量。评价标准[1]:2分(软组织结构清晰,其边缘清楚,无伪影,且诊断明确);1分(软组织结构清晰,边缘欠清,有轻度伪影,但尚可诊断);0分(软组织结构不清,边缘模糊,伪影较重,不能进行诊断)。

    统计设备记录的容积CT剂量指数(CT dose index volumes,CTDIvol)及剂量长度乘积(dose length product,DLP),并计算有效辐射剂量(effective dose,ED)[2],计算公式:

    $$ {\rm{ED}}={\rm{DLP}}\times k(k=0.011\;{\rm{mSv}}\cdot{\rm{mGy}}\cdot{\rm{cm}})。$$ (2)

    采用SPSS 26.0软件对数据进行统计学分析。连续性数据非正态分布数据两组间比较采用Mann-Whitney U检验,用中位数及四分位数(M(Q25,Q75))表示。双侧检验,以P<0.05为差异有统计学意义。

    采用组内相关系数(intraclass correlation coefficient,ICC)对3位诊断医师的评分结果一致性进行分析。ICC介于0和1之间,ICC大于0.75表示一致性较好。

    两组图像腰大肌的CT值、竖脊肌的CT值和噪声(SD2)、SNR均存在统计学差异,而腰大肌的噪声(SD1)不具有统计学差异(表1);图1为120 kV轴位上噪声和CT值测量及矢状位重组图,图2为Sn 150 kV下的轴位上噪声和CT值测量测量及矢状位重组图。

    表  1  A组和B组图像质量客观评价表
    Table  1.  Objective evaluation of image quality in groups A and B
    项目 组别统计检验
    A组B组ZP
       腰大肌/HU53.00(48.70~56.00)47.90(43.70~51.00)2.7410.016
       SD15.73(4.83~6.83)5.09(4.69~5.24)1.9040.057
       竖脊肌/HU52.00(46.2~55.00)43.50(38.20~51)3.511<0.001
       SD25.41(5.27~5.98)4.56(3.62~5.63)3.964<0.001
       SNR9.12(7.88~10.51)9.86(7.95~10.02)-0.693 0.488
    下载: 导出CSV 
    | 显示表格
    图  1  管电压120 kV下CT值和噪声测量及矢状位重组图(重组层厚1 mm、间隔0.6 mm)
    Figure  1.  CT value, noise measurement, and sagittal position recombination at 120 kV tube voltage (recombination layer thickness 1 mm, interval 0.6 mm)
    图  2  管电压Sn 150 kV下CT值和噪声测量及矢状位重组图(重组层厚1 mm、间隔0.6 mm)
    Figure  2.  CT value, noise measurement, and sagittal position recombination at tube voltage Sn 150 kV (recombination layer thickness 1 mm, interval 0.6 mm)

    3位医师对椎间盘、椎间孔、黄韧带、硬膜囊及小关节及整体图像质量评价均无统计学差异(表2),说明两组图像质量医师主观评价无差异,且均能符合医师诊断要求。

    表  2  3位诊断医师的主观评分统计分析表
    Table  2.  Statistical analysis of the subjective scores from the three doctors interpreting the computed tomography images
    指标组别P
    A组B组
    椎间盘   2.00±0.002.00±0.00>0.999
    椎间孔   1.98±0.151.98±0.15 0.156
    黄韧带   1.95±0.212.00±0.00 0.562
    硬膜囊   1.98±0.151.95±0.21>0.999
    小关节图像 2.00±0.002.00±0.00 0.320
    整体图像质量2.00±0.002.00±0.00>0.999
    下载: 导出CSV 
    | 显示表格

    两组辐射剂量DLP、ED有统计学差异,两组辐射剂量差异明显,B组DLP值比A组降低了32.27%,B组ED值比A组降低了30.31%(表3)。

    表  3  A组和B组辐射剂量统计表
    Table  3.  Radiation dose in groups A and B
    项目组别统计检验
    A组B组ZP
       mAs333.00(300.00~362.00)237.50(222.00~261.00)7.885<0.001
       CTDIvol14.75(13.65~16.00)6.57(5.20~7.23)8.015<0.001
       DLP413.60(351.00~425.50)280.13(230.89~327.20)6.946<0.001
       ED4.55(3.86~4.68)3.08(2.54~3.60)6.946<0.001
    下载: 导出CSV 
    | 显示表格

    腰椎因体层相对较厚,需要高管电压来增加X线的穿透力,高管电流来降低图像的噪声,造成腰椎CT辐射剂量往往较高,以往研究都是通过降低管电流来降低辐射剂量。随着设备和技术的进步,众多新的降低辐射剂量的技术出现,如:低管电压[3-4]、自动管电流[5-6]、高级迭代重建算法[7]、能谱纯化[8]等,这些技术为我们开展低剂量CT提供了条件。

    本研究B组管电压是用能谱纯化Sn 150 kV,而A组管电压是用120 kV,统计结果显示B组的辐射剂量低于A组30.31%。因为A组120 kV的X线球管是用铜和铝滤过,Sn 150 kV的X线球管是用能谱纯化技术的锡滤过,锡的原子序数比铜和铝高,锡滤过板能过滤掉X线球管的低能级射线,提高射线能量,而对人体产生辐射的主要是低能级软射线,低能级软射线以光电效应为主,大部分被人体吸收产生辐射。能谱纯化技术只保留了对人体成像有用的高能级射线,高能级射线会穿过人体相对辐射较少,所以B组辐射剂量低于A组,多学者也证实了这一说法[9-13]

    客观评价中A组肌肉的噪声要高于B组,腰大肌的噪声两组之间无统计学差异,而竖脊肌的噪声两组之间有统计学差异,此结果说明射线能量和图像噪声成正相关,也证实了Sn 150 kV的穿透力较120 kV的好。因竖脊肌处于腰大肌的下层,射线先穿过腰大肌再到竖脊肌,射线能量会因组织的阻挡发生衰减,A组射线的能量到达竖脊肌时比B组衰减更多,因衰减后的能量差异造成了噪声值的差异,故造成了两组不同肌肉之间统计学结果的差异。

    沈梓璇等[14]论述了120 kVp管电压所获得的腰椎图像质量评分以及信噪比皆较高,但辐射剂量也较大的观点。本文为了解决这一问题,首次采用Sn 150 kV用于腰椎CT检查,主观评价结果显示,3位观察者的ICC为0.829,表示为两组图像主观评价一致性较好,说明两组图像质量均满足诊断要求,主客观评价结果均证实了Sn 150 kV用于腰椎CT检查是可行的。王帅等[15]也证实Sn 150 kV能用于全腹部CT检查,且辐射剂量较低,与本文研究结果一致。

    高级模拟迭代重建,是将原始图像中的原始数据噪声投射到图像中,得到的图像是多次迭代重建后的组合,再将原始数据进行准确的图像校正,对原始数据域进行去噪及去除伪影,最后进行图像域的校正,反复迭代来降低噪声,图像空间分辨率不受影响。客观评价表中A组和B组图像的噪声均值都处于10以下,证实了高级模拟迭代重建的降噪能力。顾海峰等[16]和Schlunk等[17]也证明了迭代重建能降低噪声保证图像质量满足诊断需求。

    综上所述,采用能谱纯化Sn 150 kV结合ADMIRE,不但能有效减低辐射剂量,还可保证优质的图像质量,值得在成人腰椎CT中推广使用。

  • 图  1   SPEENet架构

    Figure  1.   The architecture of SPEENet

    图  2   Sobel算子的结果示意

    Figure  2.   The result of Sobel operator

    图  3   测试结果1,显示窗口为[-160,240] HU

    Figure  3.   The test result 1, and the display window is [-160,240] HU

    图  4   测试结果1的局部

    Figure  4.   The local part of the test result 1

    图  5   测试结果2,显示窗口为[-160,240] HU

    Figure  5.   The test result 2, and the display window is [-160,240] HU

    图  6   测试结果2的局部

    Figure  6.   The local part of the test result 2

    表  1   去噪结果的PSNR和SSIM

    Table  1   PSNR and SSIM of the denoised results

    方法FBPConvNetRED-CNNSPEENet1SPEENet2SPEENet
    PSNR32.10432.73133.20433.23333.072
    SSIM 0.919 0.916 0.920 0.922 0.923
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-04
  • 录用日期:  2022-07-12
  • 网络出版日期:  2022-07-19
  • 发布日期:  2022-11-02

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